emerging trends in regenerative medicine: a...

1. Introduction

2. CiteSpace

3. Disciplines and topics involved

in regenerative medicine

4. The intellectual structure of

regenerative medicine

5. Emerging trends

6. Conclusion

7. Expert opinion

Review

Emerging trends in regenerativemedicine: a scientometric analysisin CiteSpaceChaomei Chen†, Zhigang Hu, Shengbo Liu & Hung Tseng†Drexel University, College of Information Science and Technology, PA, USA

Introduction: Regenerative medicine involves research in a number of fields

and disciplines such as stem cell research, tissue engineering and biological

therapy in general. As research in these areas advances rapidly, it is critical

to keep abreast of emerging trends and critical turns of the development of

the collective knowledge.

Areas covered: A progressively synthesized network is derived from

35,963 original research and review articles that cite 3875 articles obtained

from an initial topic search on regenerative medicine between 2000 and

2011. CiteSpace is used to facilitate the analysis of the intellectual structure

and emerging trends.

Expert opinion: A major ongoing research trend is concerned with finding

alternative reprogramming techniques as well as refining existing ones for

induced pluripotent stem cells (iPSCs). A more recent emerging trend focuses

on the structural and functional equivalence between iPSCs and human

embryonic stem cells and potential clinical and therapeutic implications on

regenerative medicine in a long run. The two trends overlap in terms of

what they cite, but they are distinct and have different implications on future

research. Visual analytics of the literature provides a valuable, timely, repeat-

able and flexible approach in addition to traditional systematic reviews

so as to track the development of new emerging trends and identify

critical evidence.

Keywords: CiteSpace, co-citation analysis, induced pluripotent stem cells, regenerative

medicine, scientometrics

Expert Opin. Biol. Ther. [Early Online]

1. Introduction

Regenerative medicine is a rapidly growing and fast-moving interdisciplinary field ofstudy, involving stem cell research, tissue engineering, biomaterials, wound healingand patient-specific drug discovery [1-3]. The potential of reprogramming patients’own cells for biological therapy, tissue repairing and regeneration is critical to regener-ative medicine. It has been widely expected that regenerative medicine will revolutio-nize medicine and clinical practices far beyond what is currently possible.Mesenchymal stem cells (MSCs), for example, may differentiate into bone cells, fat cellsand cartilage cells. Skin cells can be reprogrammed into induced pluripotent stem cells(iPSCs). The rapid advance of the research has also challenged many previous assump-tions and expectations. Although iPSCs resemble embryonic stem cells (ESCs) inmanyways, comparative studies have found potentially profound differences [4-6].

The body of the relevant literature grows rapidly. In this article, unless statedotherwise, the literature is reviewed as of November 2011. The Web of Sciencehas 4295 records between 2000 and 2011 based on a topic search of the term‘regenerative medicine’ in titles, abstracts or indexing terms. If we include recordsthat are relevant to regenerative medicine, but do not use the term ‘regenerative

10.1517/14712598.2012.674507 © 2012 Informa UK, Ltd. ISSN 1471-2598 1All rights reserved: reproduction in whole or in part not permitted

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medicine’ explicitly, the number could be as 10 times higher.Stem cell research plays a substantial role in regenerativemedicine. There are more than 2 million publications onstem cells on Google Scholar. There are 167,353 publicationsspecifically indexed as related to stem cell research in the Webof Science. Keeping abreast the fast-moving body of literatureis critical not only because new discoveries emerge from adiverse range of areas but also because new findings mayfundamentally alter the collective knowledge as a whole [7].Scientometrics is a branch of informatics that quantitatively

analyzes patterns in scientific literature in order to understandemerging trends and the knowledge structure of a research

field. Science mapping tools typically take scientific publica-tions in the literature as an input and generate interactivevisual representations of complex structures for statisticalanalysis and interactive visual exploration. An increasingnumber of science mapping tools are available [8]. Sciencemapping tools resemble their counterparts in biomedicineresearch such as Cytoscape [9] and Ingenuity Pathway Analy-sis [10]. Many science mapping techniques are originatedfrom the idea of co-citation analyses, which characterize thestructure of intellectual knowledge in terms of networks ofco-cited references [11]. An array of science mapping toolsare made widely available to researchers and analysts,

Table 1. Major clusters of co-cited references.

Cluster ID Size Silhouette Label (TFIDF) Label (LLR) Label (MI) Year Ave.

9 97 0.791 Evolving concept Mesenchymal stem cell Cardiac progenitor cell 199917 71 0.929 Somatic control Drosophila spermatogenesis Drosophila 19946 67 0.980 Mcf-7 cell Intestinal-type gastric cancer Change 200112 62 0.891 Midkine Human embryonic stem cell Dna 20025 53 0.952 Grid2ip gene Silico Gastric cancer 200219 42 0.119 Bevacizumab Combination Cartilage 20047 40 0.960 Monogenic disease treatment Induced pluripotent stem cell Clinic 200815 25 0.930 Tumorigenic melanoma cell Cancer stem cell Cancer prevention 2003

Clusters are referred in terms of the labels selected by log-likelihood ratio test method (LLR).

Figure 1. Disciplines involved in regenerative medicine, shown as a Pathfinder network of subject categories.

C. Chen et al.

2 Expert Opin. Biol. Ther. [Early Online]

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notably including HistCite [12], VOSviewer [13], NetworkWorkBench [14], DIVA [15], Loet Leydesdorff’s software [16]

and CiteSpace [7,17-20].In fact, a recent citation network analysis [21] identified

future core articles on regenerative medicine based on theirpositions in a citation network derived from 17,824 articlespublished before the end of 2008. In this review, we demon-strate a scientometric approach and use CiteSpace to delineatethe structure and dynamics of the regenerative medicineresearch. CiteSpace is specifically designed to facilitate thedetection of emerging trends and abrupt changes in scientificliterature. Our study is unique in several ways. First, our data-set contains relevant articles published between 2000 and2011. We expect that it will reveal more recent trendsemerged within the last 3 years. Second, we use a citationindex-based expansion to construct our dataset, which ismore robust than defining a rapidly growing field with a listof predefined keywords. Third, emerging trends are identifiedbased on indicators computed by CiteSpace without domainexperts’ intervention or prior working knowledge of the topic.

This approach makes the analysis repeatable with new dataand verifiable by different analysts.

2. CiteSpace

CiteSpace is used to generate and analyze networks ofco-cited references based on bibliographic records retrievedfrom the Web of Science. An initial topic search for‘regenerative medicine’ resulted in 4295 records publishedbetween 2000 and 2011. After filtering out less representativerecord types such as proceedings papers and notes, thedataset was reduced to 3875 original research articles andreview articles.

The 3875 records do not include relevant publications ifthe term ‘regenerative medicine’ does not explicitly appearin the titles, abstracts or index terms. We expanded the datasetby citation indexing. If an article cites at least one of the3875 records, then the article will be included in theexpanded dataset based on the assumption that citing aregenerative medicine article makes the citing article relevant

Figure 2. Part of a minimum spanning tree of a 641-keyword network based on articles published between 2000 and 2011.

Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace

Expert Opin. Biol. Ther. [Early Online] 3

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to the topic. The citation index-based expansion resulted in35,963 records, consisting of 28,252 (78.6%) original articlesand 7711 (21.4%) review articles. The range of the expandedset remains to be 2000 -- 2011. Thus, the analysis focuses onthe development of regenerative medicine over the lastdecade. The 35,963-article dataset is used in the subsequentanalysis. Incorrect citation variants to the two highly visiblereferences, a 1998 landmark article by Thomson et al. [22]

and a 1999 article by Pittenger [23], were corrected prior tothe analysis.

3. Disciplines and topics involved inregenerative medicine

Which disciplines are involved in regenerative medicine? Eacharticle indexed by the Web of Science is assigned one or more

subject categories. Figure 1 shows a network of such subjectcategories after being simplified by Pathfinder network scal-ing, which retains the most salient connections. The mostcommon category is Cell Biology, which has the largest circle,followed by Biochemistry & Molecular Biology and MaterialsScience. Oncology, with some of its citation rings in red, is acategory in which the number of articles has increased rapidly.Although Medical Informatics, Computer Science and SocialSciences are much smaller, they are marked for reference.

The topics involved in regenerative medicine can be deli-neated in terms of the keywords assigned to each article inthe dataset. Figure 2 shows a portion of a minimum spanningtree of a network of keywords. Adjacent keywords are oftenassigned to the same articles. For example, ips cells, definedfactors and human somatic cells are near to each other onthe top of the diagram.

Table 2. Cited references and citing articles of Cluster #17 drosophila spermatogenesis.

Cluster #17 drosophila spermatogenesis

Cited References Citing Articles

Cites Author (Year) Journal,

Volume, Page

Coverage % Author (Year) Title

260 Schofield R (1978) Blood Cells, V4, P7 34 Tran J (2000) somatic control over the germlinestem cell lineage during drosophila spermatogenesis

192 Xie T (2000) SCIENCE, V290, P328 25 Spradling A (2001) stem cells find their niche151 Xie T (1998) CELL, V94, P251 23 Shinohara T (2001) remodeling of the postnatal

mouse testis is accompanied by dramatic changesin stem cell number and niche accessibility

149 Kiger AA (2001) SCIENCE, V294, P2542 20 Kiger AA (2001) stem cell self-renewal specified byjak-stat activation in response to a support cell cue

147 Tulina N (2001) SCIENCE, V294, P2546 14 Tulina N (2001) control of stem cell self-renewal indrosophila spermatogenesis by jak--stat signaling

Cluster label terms are bold.

Table 3. Cited references and citing articles of Cluster #9 mesenchymal stem cell.

Cluster #9 mesenchymal stem cell

Cited references Citing articles


Volume, Page


2486 Pittenger MF (1999) Science, V284, P143 40 Blau, HM (2001) the evolving concept of a stemcell: entity or function?

1061 Jiang YH (2002) Nature, V418, P41 15 Bianco, P (2001) stem cells in tissue engineering705 Orlic D (2001) Nature, V410, P701 9 Klich, Izabella (2010) very small embryonic like

stem cells (vsels) isolated from adult tissues -- anupdate

662 Prockop DJ (1997) Science, V276, P71 9 Guyette, Jacques P. (2010) strategies forregeneration of heart muscle

596 Asahara T (1997) Science, V275, P964 8 Yi, B. Alexander (2010) pregenerative medicine:developmental paradigms in the biology ofcardiovascular regeneration

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4. The intellectual structure of regenerativemedicine

CiteSpace represents the literature in terms of a network syn-thesized from a series of individual networks. Each individualnetwork is constructed from articles published in a 1-yeartime interval, known as a time slice. CiteSpace integrates theseindividual networks and forms an overview of how a scientificfield has been evolving over time.

For this review, an individual network is derived from the100 most cited articles published in the corresponding timeslice, which ranges from 2000 to 2011.

Each research article typically cites a number of references.These references are represented as nodes in a co-citationnetwork. The connectivity between the nodes of such refe-rences represents how often they are cited by the same articles.The assumption is that if two references are often citedtogether, then it is evident that the two references are associ-ated in some ways. The exact nature of the connectivity repre-sents a dual relationship between the cited references and theirciting articles. It has been shown that networks formed in thisway capture research focuses of the underlying scientificcommunity [7,11,24].

CiteSpace characterizes emerging trends and patterns ofchange in such networks in terms of a variety of visual attri-butes. The size of a node indicates how many citations theassociated reference received. Each node is depicted with aseries of citation tree-rings across the series of time slices.The structural properties of a node are displayed in terms ofa purple ring. The thickness of the purple ring indicates thedegree of its betweenness centrality, which is a measure asso-ciated with the transformative potential of a scientific contri-bution. Such nodes tend to bridge different stages of thedevelopment of a scientific field. Citation rings in red indicatethe time slices in which citation bursts, or abrupt increases ofcitations, are detected. Citation bursts provide a useful meansto trace the development of research focus.

CiteSpace divides the co-citation network into a number ofclusters of co-cited references such that references are tightlyconnected within the same clusters, but loosely connectedbetween different clusters. Table 1 lists eight major clusters bytheir size, that is, the number of members in each cluster. Clus-ters with few members tend to be less representative than largerclusters because small clusters are likely to be formed by the cit-ing behavior of a small number of publications. The quality of acluster is also reflected in terms of its silhouette score, which is

Figure 3. Trajectories of relevant research shown in a hybrid network of co-cited references and burst terms from titles and

abstracts. Clusters are labeled in blue text. Landmark articles are labeled in black. Burst terms are shown in light red text. Red

circles indicate articles with citation bursts, that is, rapid increases of citation counts.



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an indicator of its homogeneity or consistency. Silhouette valuesof homogenous clusters tend to close to 1. Most of the clustersin Table 3 are highly homogeneous, except Cluster #19 with alow silhouette score of 0.119. Each cluster is labeled by nounphrases from titles of citing articles of the cluster [24]. Using

terms from citing articles is in part due to the limitation ofsource data. Titles of cited references may not be always avail-able in records from the Web of Science. Labels chosen by thelog-likelihood ratio test method (LLR) are used in the subse-quent discussions [24].

Table 4. Cited references and citing articles of Cluster #12 human embryonic stem cell.

Cluster #12 human embryonic stem cell



Volume, Page


2223 Thomson JA (1998) Science, V282, P1145 32 Witkowska, Anna (2010) human embryonic stemcells -- regulation of pluripotency and differentiation

1030 Evans MJ (1981) Nature, V292, P154 29 Vazin, Tandis (2010) human embryonic stemcells:derivation, culture, and differentiation: a review

735 Martin GR (1981) P Natl Acad Sci-Biol, V78, P7634 27 Schnerch, Angelique (2010) distinguishing betweenmouse and human pluripotent stem cell regulation:the best laid plans of mice and men

708 Reubinoff BE (2000) Nat Biotechnol, V18, P399 24 Avery, Stuart (2010) the role of smad4 in humanembryonic stem cell self-renewal and stem cell fate

707 Sato N (2004) Nat Med, V10, P55 23 Ralston, Amy (2010) the genetics of inducedpluripotency

Cluster #12 human embryonic stem cells are bold.

Table 5. Cited references and citing articles of Cluster #7 on induced pluripotent stem cells (iPSCs).

Cluster #7 induced pluripotent stem cell


Cites Author (Year) Journal, Volume, Page Coverage % Author (Year) Title

1841 Takahashi K (2006) Cell, V126, P663 95 Stadtfeld, Matthias (2010) induced pluripotency: history,mechanisms, and applications

1583 Takahashi K (2007) Cell, V131, P861 80 Kiskinis, Evangelos (2010) progress toward the clinicalapplication of patient-specific pluripotent stem cells

1273 Yu JY (2007) Science, V318, P1917 77 Masip, Manuel (2010) reprogramming with definedfactors: from induced pluripotency to inducedtransdifferentiation

762 Okita K (2007) Nature, V448, P313 77 Sommer, Cesar A. (2010) experimental approaches for thegeneration of induced pluripotent stem cells

640 Wernig M (2007) Nature, V448, P318 73 Lowry, William E. (2010) roadblocks en route to theclinical application of induced pluripotent stem cells

615 Park IH (2008) Nature, V451, P141 73 Archacka, Karolina (2010) induced pluripotent stemcells -- hopes, fears and visions

501 Nakagawa M (2008) Nat Biotechnol, V26, P101 73 Yoshida, Yoshinori (2010) recent stem celladvances: induced pluripotent stem cells for diseasemodeling and stem cell-based regeneration

445 Okita K (2008) Science, V322, P949 73 Rashid, S. Tamir (2010) induced pluripotent stemcells -- alchemist’s tale or clinical reality? rid c-6368-2011

391 Maherali N (2007) Cell Stem Cell, V1, P55 68 Kun, Gabriel (2010) gene therapy, gene targeting andinduced pluripotent stem cells: applications inmonogenic disease treatment

348 Stadtfeld M (2008) Science, V322, P945 65 Robbins, Reiesha D. (2010) inducible pluripotent stemcells: not quite ready for prime time?

Cluster #7 on induced pluripotent stem cells (iPSCs) are bold.

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The average year of publication of a cluster indicates itsrecentness. For example, Cluster #9 on MSC has an averageyear of 1999. The most recently formed cluster, Cluster#7 on iPSCs, has an average year of 2008.

Cluster #7 contains numerous nodes with red rings ofcitation bursts. The visualized network also shows highlyburst terms found in the titles and abstracts of citing articlesto the major clusters. For example, terms stem-cell-renewaland germ-line-stem-cells are not only used when articles citereferences in Cluster #17 drosophila spermatogenesis, butalso used with a period of rapid increase. Similarly, the terminduced-pluripotent-stem-cells is a burst term associated withCluster #7, which is consistently labeled as iPSC by a differentselection mechanism, the LLR. We will particularly focuson Cluster #7 in order to identify emerging trends inregenerative medicine.

Figure 3 shows an overview of a network of co-citedreferences and burst terms on regenerative medicine. Eachcluster involves citing articles as well as cited references. Thefollowing tables list key players of four major clusters #17,#9, #12 and #7 to indicate their critical research focuses.Five citing articles that the most references in a cluster areselected, whereas five cited references that have the mostcitations are highlighted. Clusters #5 and #6 appear to bealmost exclusively formed by the publications of Katoh M.

The core members of Cluster #9 represent major mile-stones in relation to MSCs, notably Pittenger et al.’s articleon Multilineage Potential of Adult Human Mesenchymal StemCells [23] and an article by Jiang et al., entitled Pluripotencyof mesenchymal stem cells derived from adult marrow [25].

Cluster #12, human hESCs, contains further milestones instem cell research. The second most highly cited work in this

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

#2 signaling network

#4 stem cell differentiation

#7 induced pluripotent stem cell

#9 mesencymal stem cell

#12 human embryonic stem cell

#13 dna methylation analysis

#15 cancer stem cell

#17 drosophila spermatogenesis

Figure 4. Timelines of co-citation clusters. Major clusters are labeled on the right. Landmark articles are labeled.

Table 6. Cited citations with the highest betweenness centrality.

Rank Centrality References Cluster #

1 0.55 Thomson JA, 1998, Science, V282, P1145 122 0.32 Bjornson CRR, 1999, Science, V283, P534 93 0.18 Evans MJ, 1981, Nature, V292, P154 124 0.17 Reya T, 2001, Nature, V414, P105 155 0.15 Kiger AA, 2000, Nature, V407, P750 176 0.13 Pittenger MF, 1999, Science, V284, P143 97 0.11 Takahashi K, 2006, Cell, V126, P663 78 0.11 Reubinoff BE, 2000, Nat Biotechnol, V18, P399 129 0.10 Martin GR, 1981, Proc Natl Acad Sci-Biol, V78, P7634 12

They are central in connecting the network’s components.



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cluster is the 1981 article by Evans and Kaufman (Table 4) [26].Their work pioneered the establishment of pluripotent celllines in tissue culture from mouse blastocysts. The predomi-nant member of the cluster is the 1998 landmark article byThomson et al. As of November 2011, it has 2223 citationsin the Web of Science. Their work reported the establishmentof pluripotent cell lines from human blastocysts and openedup new grounds for human developmental biology, drugdiscovery and transplantation medicine.The five major citing articles, in terms of their citation

coverage, are all published in 2010,more than 10 years later afterthe human blastocyst-derived pluripotent cell lines and 20 yearssince the first mouse blastocyst-derived pluripotent cell lines.Cluster #7 is the most recently formed cluster. We selected

10 most cited references in this cluster and 10 citing articles(Table 5).The most cited article in this cluster, Takahashi 2006 [27],

demonstrated how pluripotent stem cells can be directlygenerated from mouse somatic cells by introducing only afew defined factors as opposed to transferring nuclear contents

to oocytes or egg cells. Their work is a major milestone. Thesecond most cited reference [28], from the same group ofresearchers, further advanced the state of the art by demon-strating how differentiated human somatic cells can bereprogrammed into pluripotent stem cells using the samefactors identified in their previous work.

Cluster #7 consists of 40 co-cited references. The 10selected citing articles are all published in 2010. They cited65 -- 95% of these references. The one that has the highestcitation coverage of 95% is an article by Stadtfeld et al. Unlikeworks that aim to refine and improve the ways to produceiPSCs, their primary concern was whether iPSCs are equiva-lent, molecularly and functionally, to blastocyst-derivedESCs. The Stadtfeld article itself belongs to the cluster. Otherciting articles also seem to question some of the fundamentalassumptions or call for more research before further clinicaldevelopment in regenerative medicine.

Figure 4 shows a timeline visualization of how the networkis divided into distinct co-citation clusters. It is evident thatCluster #7 has a high concentration of nodes with citationbursts, which echoes the fact that this is the most recentlyformed cluster. Clusters #9 and #17 do not appear to havemuch current high-profile publications. In addition to Cluster#7, Clusters #11 -- #13 appear to have recent publicationswith citation bursts.

4.1 Betweenness centralityThe betweenness centrality of a node in the network measuresthe importance of the position of the node in the network.Two types of nodes may have high betweenness centralityscores:

1) Nodes that are highly connected to other nodes suchas hubs.

2) Nodes that are positioned between different groupsof nodes.

We are particularly interested in the second type becausethey are more likely to lead to insights into emerging trendsthan the first type of nodes.

Table 6 shows nine structurally essential references in thesynthesized network. These references are important in termsof not only how they connect individual nodes in the networkbut also how they connect aggregated groups of nodes, such asco-citation clusters. Three of these nodes are in Cluster#12 and two in Cluster #2. These works can be seen as land-mark works in the context of our broadly defined area ofregenerative medicine.

4.2 Most cited articlesThe most cited articles are usually regarded as the landmarksdue to their groundbreaking contributions. Cluster #7 has3 articles in the top 10 landmark articles. Each of Clusters#9, #12 and #15 has two. The most cited article in our datasetis Pittenger MF (1999) with 2486 citations, followed by

Table 7. Most cited references.

Citation counts References Cluster #

2486 Pittenger MF, 1999,Science, V284, P143

9

2223 Thomson JA, 1998,Science, V282, P1145

12

2102 Reya T, 2001, Nature,V414, P105 [Review]

15

1841 Takahashi K, 2006,Cell, V126, P663

7

1583 Takahashi K, 2007,Cell, V131, P861

7

1273 Yu JY, 2007, Science,V318, P1917

7

1145 Jain RK, 2005, Science,V307, P58

19

1061 Jiang YH, 2002, Nature,V418, P41

9

1030 EVANS MJ, 1981, Nature,V292, P154

12

945 Al-Hajj M, 2003, Proc NatlAcad Sci USA, V100, P3983

15

Table 8. References with the strongest citation bursts.

Citation

bursts

References Cluster #

124.73 Takahashi K, 2006, Cell, V126, P663 7121.36 Takahashi K, 2007, Cell, V131, P861 781.37 Yu JY, 2007, Science, V318, P1917 771.24 Okita K, 2008, Science, V322, P949 766.23 Meissner A, 2008, Nature, V454, P766 1363.12 Vierbuchen T, 2010, Nature, V463, P1035 862.54 Zhou HY, 2009, Cell Stem Cell, V4, P381 7

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Thomson JA (1998) with 2223 citations. The third one is areview article by Reya T (2001). Articles at the fourth to sixthpositions are all from Cluster #7, namely Takahashi K (2006),Takahashi K (2007) and Yu JY (2007). These three are alsothe more recent articles on the list, suggesting that they haveinspired intense interest in iPSCs.

4.3 Citation burstsA citation burst has two attributes: the intensity of the burstand how long the burst status lasts. Table 8 lists referenceswith the strongest citation bursts across the entire datasetduring the period of 2000 -- 2011. The first four articleswith strong citation bursts are from Cluster #7 on iPSCs.Interestingly, one 2009 article (again in Cluster #7) and one2010 article (in Cluster #8, a small cluster) are detected tohave considerable degrees of citation burst.

4.4 SigmaThe Sigma metric measures both structural centrality and cita-tion burstness of a cited reference. If a reference is strong inboth measures, it will have a higher Sigma value than a referencethat is only strong in one of the two measures (Table 9).

The pioneering iPSCs article by Takahashi (2006) has thehighest Sigma of 377340.46, which means it is structurallyessential and inspirational in terms of its strong citation burst.The second highest work by this measure is a 1999 article inScience by Bjornson et al. [29]. They reported an experimentin which neural stem cells were found to have a widerdifferentiation potential than previously thought becausethey evidently produced a variety of blood cell types.

5. Emerging trends

The modularity of a network measures the degree to whichnodes in the network can be divided into a number of groupssuch that nodes within the same group are connected tighterthan nodes between different groups. The collective intellec-tual structure of the knowledge of a scientific field can berepresented as associated networks of co-cited references.Such networks evolve over time. Newly published articlesmay introduce profound structural variation or have little orno impact on the structure.

Figure 5 shows the change of modularity of networks overtime. Each network is constructed based on a 2-year sliding

Table 9. Structurally and temporally significant references.

Sigma Burst Centrality Citations References Cluster #

377340.46 124.73 0.11 1841 Takahashi K, 2006, Cell, V126, P663 729079.18 37.38 0.32 202 Bjornson CRR, 1999, Science, V283, P534 9195.15 121.36 0.04 1583 Takahashi K, 2007, Cell, V131, P861 758.91 81.37 0.05 1273 Yu JY, 2007, Science, V318, P1917 715.97 19.53 0.15 130 Kiger AA, 2000, Nature, V407, P750 17

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Figure 5. The modularity of the network dropped considerably in 2007 and even more in 2009, suggesting that some major

structural changes took place in these 2 years in particular.



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window. The number of publications per year increasedconsiderably. It is noticeable that the modularity dipped in2007 and bounced back to the previous level before itdropped even deeper in 2009. Based on this observation, itis plausible that groundbreaking works appeared in2007 and 2009. We will, therefore, specifically investigatepotential emerging trends in these 2 years.Which publications in 2007 would explain the significant

decrease in the modularity of the network formed based onpublications prior to 2007? If a 2007 publication has a subse-quent citation burst, then we expect that this publicationplayed an important role in changing the overall intellectualstructure. Eleven publications in 2007 are found to have sub-sequent citation bursts (Table 10). Notably, Takahashi2007 and Yu 2007 top the list. Both of them representpioneering investigations of reprogramming human body cellsto iPSCs. Both of them have current citation bursts since2009. Other articles on the list address the pluripotency ofstem cells related to human cancer, including colon cancerand pancreatic cancer. Two review articles on regenerativemedicine and tissue repair are published in 2007 with citationbursts since 2010. These observations suggest that the modu-larity change in 2007 is an indication of an emerging trend inthe human iPSC research. The trend is current and active as

shown by the number of citation bursts associated withpublications in 2007 alone.

If the modularity change in 2007 indicates an emergingtrend in human iPSCs research, what caused the even moreprofound modularity change in 2009? The cluster that isresponsible for the 2009 modularity change is Cluster#7 iPSC. On the one hand, the cluster contains Takahashi2006 and Takahashi 2007, which pioneered the humaniPSC trend. On the other hand, the cluster contains manyrecent publications. The average age of the articles in this clus-ter is 2008. Therefore, we examine the members of this clusterclosely, especially focusing on 2009 publications.

The impact of Takahashi 2006 and Takahashi 2007 is soprofound that their citation rings would overshadow all othermembers in Cluster #7. After excluding the display of theirovershadowing citation rings, it becomes apparent that thiscluster is full of articles with citation bursts, which are shownas citation rings in red. We labeled the ones published in2009 and also two 2008 articles and one 2010 article (Figure 6and Table 11).

The pioneering reprogramming methods introduced byTakahashi 2006 and Takahashi 2007 modify adult cells toobtain properties similar to ESCs using a cancer-causingoncogene c-Myc as one of the defined factors and a virus to

Table 10. Articles published in 2007 with subsequent citation bursts in descending order of local citation counts.

Ref. Local citations Title Burst Duration Range (2000 -- 2011)

Takahashi 2007 [28] 1583 Induction of Pluripotent Stem Cells fromAdult Human Fibroblasts by DefinedFactors

121.36 2009 -- 2011

Yu 2007 [39] 1273 Induced Pluripotent Stem Cell LinesDerived from Human Somatic Cells

81.37 2009 -- 2011

Wernig 2007 [40] 640 In vitro reprogramming of fibroblasts intoa pluripotentES-cell-like state

26.70 2008 -- 2009

O’Brien 2007 [41] 438 A human colon cancer cell capable ofinitiating tumour growth inimmunodeficient mice

18.13 2008 -- 2009

Ricci-Vitiani 2007 [42] 427 Identification and expansion of humancolon-cancer-initiating cells

8.83 2008 -- 2009

Li 2007 [43] 299 Identification of Pancreatic Cancer StemCells

9.78 2008 -- 2008

Mikkelsen 2007 [44] 283 Genome-wide maps of chromatin state inpluripotent and lineage-committed cells

19.59 2010 -- 2011

Laflamme 2007 [45] 265 Cardiomyocytes derived from humanembryonic stem cells in pro-survival factors enhance function ofinfarcted rat hearts

16.48 2010 -- 2011

Gimble 2007 [46] [R] 247 Adipose-Derived Stem Cells forRegenerative Medicine

25.19 2010-- 2011

Phinney 2007 [47] [R] 229 Concise Review: Mesenchymal Stem/Multipotent Stromal Cells: The State ofTransdifferentiation and Modes of TissueRepair----Current Views

16.52 2010 -- 2011

Khang 2007 [48] [In Korean] 90 Recent and future directions of stem cellsfor the application of regenerativemedicine

35.25 2008 -- 2009

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deliver the genes into target cells [30]. It was shown later onthat c-Myc is not needed. The use of viruses as the deliveryvehicle raised safety concerns of its clinical implications inregenerative medicine because viral integration into targetcells’ genome might activate or inactivate critical host genes.Searching for virus-free techniques motivated a series of suchstudies, leading by an article [31] appeared on 9 October 2008.

What many of these 2009 articles have in common appearto be the focus on improving previous techniques of reprog-ramming human somatic cells to regain a pluripotent state.It was realized that the original method used to induce pluri-potent stem cells has a number of possible drawbacks associ-ated with the use of viral reprogramming factors. Severalsubsequent studies investigated alternative ways to inducepluripotent stem cells with lower risks or improved certainty.These articles were published within a short period oftime. For instance, Woltjen 2009 demonstrated a virus-inde-pendent simplification of iPSC production. On 26 March2009, Yu et al.’s article demonstrated that reprogramminghuman somatic cells can be done without genomic integrationor the continued presence of exogenous reprogrammingfactors. On 23 April 2009, Zhou et al.’s article demonstratedhow to avoid using exogenous genetic modifications by deli-vering recombinant cell-penetrating reprogramming proteinsdirectly into target cells. Soldner 2009 reported a methodwithout using viral reprogramming factors. Kaji reported avirus-free pluripotency induction method. On 28 May2009, Kim et al.’s article introduced a method of directdelivery of reprogramming proteins.

Vierbuchen 2010 is one of the few most recent articles thatare found to have citation bursts. The majority of the2009 articles with citation bursts focused on reprogramminghuman somatic cells to an undifferentiated state. By contrast,Vierbuchen 2010 expanded the scope of reprogramming bydemonstrating the possibility of converting fibroblasts tofunctional neurons directly.

Two articles of particular interest appear at the lower end ofCluster #7, Chin et al. 2009 [4] and Stadtfeld et al. 2010.Chin et al.’s article has 158 citations within the dataset.A citation burst was detected for Chin 2009 since 2010.Chin et al. questioned whether iPSCs are indistinguishablefrom ESCs. Their investigation suggested that iPSCs shouldbe considered as a unique subtype of pluripotent cell.

The co-citation network analysis has identified severalarticles that cite the work by Chin et al. In order to establishwhether Chin et al. represents the beginning of a newemerging trend, we inspect these citing articles listedin Table 12. Stadtfeld 2010 is the most cited citing articleby itself with 134 citations. Similar to Chin et al., Stadtfeld2010 addresses the question whether iPSCs are molecularlyand functionally equivalent to blastocyst-derived ESCs.Their work identified the role of Dlk1--Dio3 gene clusterin association with the level of induced pluripotency. Inother words, these studies focus on mechanisms that governinduced pluripotency, which can be seen as a distinct trendfrom the earlier trend on improving reprogrammingtechniques. Table 12 includes two review articles cited byStadtfeld 2010.

Figure 6. Many members of Cluster #7 are found to have citation bursts, shown as citation rings in red. Chin MH 2009 and

Stadtfeld M 2010 at the bottom area of the cluster represent a theme that differs from other themes of the cluster.



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The new emerging trend is concerned with the equivalenceof iPSCs and their hESC counterparts in terms of theirshort- and long-term functions. The new trend has criticalimplications on the therapeutic potential of iPSCs. In addi-tion to the works by Chin et al. and Stadtfeld et al., an articlepublished on 2 August 2009, by Boland et al. [32] reported aninvestigation of mice derived entirely from iPSCs. Anotherarticle [5] appeared on 12 February 2010, investigated abnor-malities such as limited expansion and early senescence foundin human iPSCs. The Stadtfeld 2010 article [6] we discussedearlier appeared on 13 May 2010.Some of the more recent citing articles of Chin et al. focused

on providing resources for more stringent evaluative and com-parative studies of iPSCs. On 7 January 2011, an article [33]

reported a study of genomic stability and abnormalities in plu-ripotent stem cells and called for frequent genomic monitoringto assure phenotypic stability and clinical safety. On 4 February2011, Bock et al. [34] published genome-wide reference maps ofDNA methylation and gene expression for 20 previously

derived human ES lines and 12 human iPS cell lines. In amore recent article [35] published on 11 February 2011,Boulting et al. established a robust resource that consists of16 iPSC lines and a stringent test of differentiation capacity.

iPSCs are characterized by their self-renewal and versatileability to differentiate into a wide variety of cell types. Theseproperties are invaluable for regenerative medicine. However,the same properties also make iPSCs tumorigenic or cancerprone. In a review article published in April 2011,Ben-David and Benvenisty [36] reviewed the tumorigenicityof human embryonic and iPS cells. Zhao et al. challenged agenerally held assumption concerning the immunogenicityof iPSCs in an article [37] on 13 May 2011. The immunoge-nicity of iPSCs has clinical implications on therapeuticallyvaluable cells derived from patient-specific iPSCs.

In summary, a series of more recent articles have re-examined several fundamental assumptions and properties ofiPSCs with more profound considerations for clinical andtherapeutic implications on regenerative medicine [38].

Table 11. Articles published in 2009 with citation bursts.

Ref. Local citations Title Burst Burst duration Range (2000 -- 2011)

Woltjen 2009 [49] 320 piggyBac transposition reprogramsfibroblasts to induced pluripotent stemcells

52.65 2009 -- 2011

Yu 2009 [50] 300 Human Induced Pluripotent Stem CellsFree of Vector and TransgeneSequences

59.97 2010 -- 2011

Zhou 2009 [51] 293 Generation of Induced PluripotentStem Cells Using Recombinant Proteins

62.54 2010 -- 2011

Soldner 2009 [52] 288 Parkinson’s Disease Patient-Derived Induced Pluripotent Stem CellsFree of Viral Reprogramming Factors

53.94 2010 -- 2011

Kaji 2009 [53] 284 Virus-free induction of pluripotencyand subsequent excision ofreprogramming factors

46.71 2009 -- 2011

Kim 2009 [54] 235 Generation of Human InducedPluripotent Stem Cells by DirectDelivery of Reprogramming Proteins

56.03 2010 -- 2011

Ebert 2009 [55] 211 Induced pluripotent stem cells from aspinal muscular atrophy patient

41.91 2010 -- 2011

Kim 2009 [56] 194 Oct4-Induced Pluripotency in AdultNeural Stem Cells

31.87 2009 -- 2011

Vierbuchen 2010 [57] 193 Direct conversion of fibroblasts tofunctional neurons by defined factors

63.12 2010 -- 2011

Lister 2009 [58] 161 Human DNA methylomes at baseresolution show widespreadepigenomic differences

51.93 2010 -- 2011

Chin 2009 [4] 158 Induced Pluripotent Stem Cells andEmbryonic Stem Cells AreDistinguished by Gene ExpressionSignatures

45.39 2010 -- 2011

Discher 2009 [59] 149 Growth Factors, Matrices, and ForcesCombine and Control Stem Cells

43.14 2010 -- 2011

Hong 2009 [60] 138 Suppression of induced pluripotentstem cell generation by thep53--p21 pathway

43.71 2010 -- 2011

Slaughter 2009 [61] 97 Hydrogels in Regenerative Medicine 31.68 2010 -- 2011

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It is also possible to place the analysis in a broadercontext by taking into account the citation behavior of morepublications each year. For example, Figure 7 shows an exten-sive network of 18,811 references shaped by the citationbehavior of 4000 publications each year from 2000 till2011 in relation to regenerative medicine. The colors indicate

the time of publication. Early publications are in darkercolors, whereas more recent ones are in yellow and orange col-ors. Labels on the map highlight the names of authors of themost highly cited references. The area that corresponds tothe iPSC cluster is located at the upper left corner of the net-work in orange, where the names of Takahashi and Yu are

Table 12. Articles that cite Chin et al.’s 2009 article [4] and their citation counts as of November 2011.

Article Citations Title

Stadtfeld 2010 [6] 134 Aberrant silencing of imprinted genes on chromosome 12qF1 in mouse induced pluripotentstem cells

Boland 2009 [32] 109 Adult mice generated from induced pluripotent stem cellsFeng 2010 [5] 72 Hemangioblastic Derivatives from Human Induced Pluripotent Stem Cells Exhibit Ltd

Expansion and Early SenescenceKiskinis 2010 [62] [R] 59 Progress toward the clinical application of patient-specific pluripotent stem cellsLaurent 2011 [33] 48 Dynamic Changes in the Copy Number of Pluripotency and Cell Proliferation Genes in

Human ESCs and iPSCs during Reprogramming and Time in CultureBock 2011 [34] 31 Reference Maps of Human ES and iPS Cell Variation Enable High-

Throughput Characterization of Pluripotent Cell LinesZhao 2011 [37] 22 Immunogenicity of induced pluripotent stem cellsBoulting 2011 [35] 17 A functionally characterized test set of human induced pluripotent stem cellsYoung 2011 [63] [R]* 16 Control of the Embryonic Stem Cell StateBen-David 2011 [36] [R]* 11 The tumorigenicity of human embryonic and induced pluripotent stem cells

*Cited by Stadtfeld 2010 [6].

[R]: Review articles.

Figure 7. An extensive network of 18,811 references cited by 4000 publications in regenerative medicine each year from

2000 till 2011.



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labeled. Networks visualized at this level may provide a goodstarting point to make sense of the dynamics of the evolvingfield. On the other hands, the devils are in the details. Differ-entiating topics, hypotheses and findings at documentand predicate is essential to the study of an evolvingscientific field.

6. Conclusion

In conclusion, the analysis of the literature of regenerativemedicine and a citation-based expansion has outlined the evo-lutionary trajectory of the collective knowledge over the lastdecade and highlighted the areas of active pursuit. Emergingtrends and patterns identified in the analysis are based oncomputational properties selected by CiteSpace, which isdesigned to facilitate sense-making tasks of scientific frontiersbased on relevant domain literature.Regenerative medicine is a fascinating and a fast-moving

subject matter. As information scientists, we have demon-strated a scientometric approach to tracking the advance ofthe collective knowledge of a dynamic scientific communityby tapping into what experts in the domain have publishedin the literature and how information and computationaltechniques can help us to discern patterns and trends at vari-ous levels of abstraction, namely, cited references and clustersof co-cited references.

7. Expert opinion

It is worth noting that this expert opinion is solely based onscientometric patterns revealed by CiteSpace without priorworking experience in the regenerative medicine field. Basedon the analysis of structural and temporal patterns of citationsand co-citations, we have identified two major emerging trends.The first one started in 2007 with pioneering works onhuman iPSCs, including subsequently refined and alternativetechniques for reprogramming. The second one started in2009 with an increasingly broad range of examinations andreexaminations of previously unchallenged assumptions withclinical and therapeutic implications on regenerative medicine,including tumorigenicity and immunogenicity of iPSCs.The referential expansion of the original topic search of

regenerative medicine has revealed a much wider spectrumof intellectual dynamics. The visual analysis of the broaderdomain outlines the major milestones throughout theextensive period of 2000 -- 2011. Several indicators and

observations converge to the critical and active role of Cluster#7 on iPSCs. By tracing interrelationships along citation linksand citation bursts, visual analytic techniques of scientomet-rics are able to guide our attention to some of the most vibra-ting and rapidly advancing research fronts and identify thestrategic significance of various challenges addressed by highlyspecialized technical articles. The number of review articles onrelevant topics is rapidly increasing, which is also a sign thatthe knowledge of regenerative medicine has been advancingrapidly. We expect that visual analytic tools as we utilized inthis review will play a more active role in supplement to tradi-tional review and survey articles. Visual analytic tools can bevaluable in finding critical developments in the vast amountof newly published studies.

The key findings of the regenerative medicine and relatedresearch over the last decade have shown that regenerativemedicine has become more and more feasible in many areasand that it will ultimately revolutionize clinical and healthcarepractice and many aspects of our society. On the other hand,the challenges ahead are enormous. The biggest challenge isprobably related to the fact that human beings are a complexsystem in that a local perturbation may lead to unpredictableconsequences in other parts of the system, which in turn mayaffect the entire system. The state of the art in science andmedicine has a long way to go to handle such complex systemsin a holistic way. Suppressing or activating a seeminglyisolated factor may have unforeseen consequences.

The two major trends identified in this review have distinctresearch agendas as well as different perspectives and assump-tions. In our opinion, the independencies of such trends at astrategic level are desirable at initial stages of these emergingtrends so as to maximize the knowledge gain that is unlikelyto be achieved by a single line of research alone. In a longrun, more trends are expected to emerge from probably theleast expected perspectives. Existing trends may be accommo-dated by new levels of integration. We expect that safetyand uncertainty will remain to be the central concern ofregenerative medicine.

Declaration of interest

H Tseng works at NIAMS/NIH, his view expressed here ispersonal and not representing any official policy of NIAMS/NIH. None of the other authors have any conflicts of interestto disclose.

C. Chen et al.


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AffiliationChaomei Chen†1, Zhigang Hu2, Shengbo Liu2 &

Hung Tseng3

†Author for correspondence1Drexel University,

College of Information Science and Technology,

3141 Chestnut Street,

Philadelphia PA 19104-2875, USA

E-mail: [email protected] University of Technology,

WISELAB, Dalian, China3National Institute of Arthritis Musculoskeletal

and Skin Diseases,

National Institute of Health,

Division of Skin and Rheumatic Diseases,

6701 Democracy Boulevard,

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